A Machine Learning Approach for Predicting Bank Customer Behavior in the Banking Industry

A Machine Learning Approach for Predicting Bank Customer Behavior in the Banking Industry

Siu Cheung Ho, Kin Chun Wong, Yuen Kwan Yau, Chi Kwan Yip
Copyright: © 2019 |Pages: 27
DOI: 10.4018/978-1-5225-8100-0.ch002
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Abstract

Currently, Chinese commercial banks are facing extremely tremendous pressure, including financial disintermediation, interest rate marketization, and internet finance. Meanwhile, increasing financial consumption demand of customers further intensifies the competition among commercial banks. Hence, it is very important to store, process, manage, and analyze the data to extract knowledge from the customer to predict their investment direction in future. Customer retention and fraud detection are the main information for the bank to predict customer behavior. It may involve the privacy data and sensitive data of the customer. Data security and data protection for the machine learning prediction is necessary before data collection. The research is focused on two parts: the first part is data security of machine learning and second part is machine learning prediction. The result is to prove the data security for the machine learning is important. Using different machining learning analysis tool to enhance the performance and reliability of machine learning applications, the customer behavior prediction accuracy can be enhanced.
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Challenge Of Chinese Banking Industry

Accuracy customer data prediction was essential for planning the business. After that, being armed with information about customer behaviors, interactions, and preferences, data specialists with the help of accurate machine learning models could unlock new revenue opportunities for banks by isolating and processing only this most relevant clients’ information to improve business decision-making. This was a challenge to predict the customer pattern and behavior for planning the business in advance in this dynamic competition environment. Zhenyu (Zhenyu et al, 2018) stated that “Machine learning is one of the most prevalent techniques in recent decades which has been widely applied in various fields. Among them, the applications that detect and defend potential adversarial attacks using machine learning method provide promising solutions in cybersecurity.” The application of machine learning on cybersecurity and reliability and security of machine learning system was conducted and analyzed the potential security threats against a machine learning approach in three phases of testing, training and data privacy.

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